Distribution harmonic state estimation based on a hybrid PSO and SA algorithm considering parameters uncertainty

Arefi, Ali, Haghifam, Mahmood Reza, & Fathi, Seyed Hamid (2011) Distribution harmonic state estimation based on a hybrid PSO and SA algorithm considering parameters uncertainty. In 2011 IEEE PES General Meeting, 24 - 28 July 2011, Detroit, Michigan.


This paper presents a new algorithm based on a Hybrid Particle Swarm Optimization (PSO) and Simulated Annealing (SA) called PSO-SA to estimate harmonic state variables in distribution networks. The proposed algorithm performs estimation for both amplitude and phase of each harmonic currents injection by minimizing the error between the measured values from Phasor Measurement Units (PMUs) and the values computed from the estimated parameters during the estimation process. The proposed algorithm can take into account the uncertainty of the harmonic pseudo measurement and the tolerance in the line impedances of the network as well as uncertainty of the Distributed Generators (DGs) such as Wind Turbines (WT). The main feature of proposed PSO-SA algorithm is to reach quickly around the global optimum by PSO with enabling a mutation function and then to find that optimum by SA searching algorithm. Simulation results on IEEE 34 bus radial and a realistic 70-bus radial test networks are presented to demonstrate the speed and accuracy of proposed Distribution Harmonic State Estimation (DHSE) algorithm is extremely effective and efficient in comparison with the conventional algorithms such as Weight Least Square (WLS), Genetic Algorithm (GA), original PSO and Honey Bees Mating Optimization (HBMO) algorithm.

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ID Code: 69031
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Harmonic state estimation, Distributed generators, Uncertainty analysis, Particle swarm optimization, Simulated annealing, Distribution networks
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2011 IEEE
Deposited On: 23 Mar 2014 23:55
Last Modified: 12 Dec 2015 02:42

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